# Import data from .RData
#load("data/PSZ_cont_snotel.RData")
load("data/cont_snotel.RData")
# Count the number of SNOTEL Sites per Ecoregion
eco_sntl_sites <- cont_snotel %>%
group_by(US_L3NAME, US_L3CODE) %>%
summarize(eco_sntl_sites = n_distinct(site_id)) %>%
filter(eco_sntl_sites >= 5)
## `summarise()` has grouped output by 'US_L3NAME'. You can override using the
## `.groups` argument.
# All SNOTEL Sites, Calculate spring days with new SWE and new SWE depth
# filter by date (March 1st - 60/61, March 15th - 74/75, April 1st - 91/92, April 15th - 105/106, August 1st - 243/244)
# Filter data by date from 2000 to present and from March 1st to August 1st
# group by year and site
start_year = 2000
# start_day = 60 #March 1
# cont_snotel_spring_mar1 <- cont_snotel %>%
# mutate(date = as.POSIXct(date, format = "%Y-%m-%d"),yday = yday(date), year = year(date)) %>%
# mutate(newSWE = ifelse(snow_water_equivalent>lag(snow_water_equivalent), snow_water_equivalent-lag(snow_water_equivalent), 0)) %>%
# filter(state != "SD", year >= start_year, ifelse(leap_year(year) == FALSE, yday >= start_day & yday <= 243,
# yday >= start_day+1 & yday <= 243+1)) %>%
# select(-c("X", "network", "description", "start", "end"))
#
# start_day = 74 #March 15
# cont_snotel_spring_mar15 <- cont_snotel %>%
# mutate(date = as.POSIXct(date, format = "%Y-%m-%d"),yday = yday(date), year = year(date)) %>%
# mutate(newSWE = ifelse(snow_water_equivalent>lag(snow_water_equivalent), snow_water_equivalent-lag(snow_water_equivalent), 0)) %>%
# filter(state != "SD", year >= start_year, ifelse(leap_year(year) == FALSE, yday >= start_day & yday <= 243,
# yday >= start_day+1 & yday <= 243+1)) %>%
# select(-c("X", "network", "description", "start", "end"))
start_day = 91 #April 1
cont_snotel_spring_apr1 <- cont_snotel %>%
mutate(date = as.POSIXct(date, format = "%Y-%m-%d"),yday = yday(date), year = year(date)) %>%
mutate(newSWE = ifelse(snow_water_equivalent>lag(snow_water_equivalent),
snow_water_equivalent-lag(snow_water_equivalent), 0),
newSWE = ifelse(temperature_min > 0, 0, newSWE)) %>%
filter(state != "SD", year >= start_year, ifelse(leap_year(year) == FALSE, yday >= start_day & yday <= 243,
yday >= start_day+1 & yday <= 243+1)) %>%
select(-c("X", "network", "description", "start", "end"))
# start_day = 105 #April 15
# cont_snotel_spring_apr15 <- cont_snotel %>%
# mutate(date = as.POSIXct(date, format = "%Y-%m-%d"),yday = yday(date), year = year(date)) %>%
# mutate(newSWE = ifelse(snow_water_equivalent>lag(snow_water_equivalent)+3, snow_water_equivalent-lag(snow_water_equivalent), 0)) %>%
# filter(state != "SD", year >= start_year, ifelse(leap_year(year) == FALSE, yday >= start_day & yday <= 243,
# yday >= start_day+1 & yday <= 243+1)) %>%
# select(-c("X", "network", "description", "start", "end"))
# # Determine number of days with increased SWE per spring at each site (YEARLY - March 1)
# cont_snotel_site_yr_mar1 <- cont_snotel_spring_mar1 %>%
# group_by(site_id, site_name, state, year, latitude, longitude, elev, US_L3CODE, US_L3NAME, L3_KEY) %>%
# summarise(newSWE_days = sum(newSWE>0, na.rm = TRUE), newSWE = sum(newSWE, na.rm = TRUE)) %>%
# merge(., eco_sntl_sites, by = c("US_L3NAME")) %>%
# mutate(eco_sntl = paste0(L3_KEY, ' (N = ', eco_sntl_sites, ')'), start = "3/1")
#
# # Determine number of days with increased SWE per spring at each site (YEARLY - March 15)
# cont_snotel_site_yr_mar15 <- cont_snotel_spring_mar15 %>%
# group_by(site_id, site_name, state, year, latitude, longitude, elev, US_L3CODE, US_L3NAME, L3_KEY) %>%
# summarise(newSWE_days = sum(newSWE>0, na.rm = TRUE), newSWE = sum(newSWE, na.rm = TRUE)) %>%
# merge(., eco_sntl_sites, by = c("US_L3NAME")) %>%
# mutate(eco_sntl = paste0(L3_KEY, ' (N = ', eco_sntl_sites, ')'), start = "3/15")
# Determine number of days with increased SWE per spring at each site (YEARLY - April 1)
cont_snotel_site_yr_apr1 <- cont_snotel_spring_apr1 %>%
group_by(site_id, site_name, state, year, latitude, longitude, elev, US_L3CODE, US_L3NAME, L3_KEY) %>%
summarise(newSWE_days = sum(newSWE>0, na.rm = TRUE), newSWE = sum(newSWE, na.rm = TRUE)) %>%
merge(., eco_sntl_sites, by = c("US_L3NAME", "US_L3CODE")) %>%
mutate(eco_sntl = paste0(L3_KEY, ' (N = ', eco_sntl_sites, ')'), start = "4/1")
## `summarise()` has grouped output by 'site_id', 'site_name', 'state', 'year',
## 'latitude', 'longitude', 'elev', 'US_L3CODE', 'US_L3NAME'. You can override
## using the `.groups` argument.
eco_sntl_sites_med <- cont_snotel_site_yr_apr1 %>%
group_by(eco_sntl) %>%
summarize(median_spring_SWE = median(newSWE_days, na.rm = T))
# # Determine number of days with increased SWE per spring at each site (YEARLY - April 15)
# cont_snotel_site_yr_apr15 <- cont_snotel_spring_apr15 %>%
# group_by(site_id, site_name, state, year, latitude, longitude, elev, US_L3CODE, US_L3NAME, L3_KEY) %>%
# summarise(newSWE_days = sum(newSWE>0, na.rm = TRUE), newSWE = sum(newSWE, na.rm = TRUE)) %>%
# merge(., eco_sntl_sites, by = c("US_L3NAME")) %>%
# mutate(eco_sntl = paste0(L3_KEY, ' (N = ', eco_sntl_sites, ')'), start = "4/15")
# Merge the DF's into one large datafame for plotting
# cont_snotel_site_yr <- rbind(cont_snotel_site_yr_mar1, cont_snotel_site_yr_mar15, cont_snotel_site_yr_apr1, cont_snotel_site_yr_apr15)
# Determine average number of days with increased SWE per spring at each site in the PSZ (SITE - April 1)
cont_snotel_site_apr1 <- cont_snotel_site_yr_apr1 %>%
group_by(site_id, site_name, state, latitude, longitude, elev, US_L3CODE, US_L3NAME, L3_KEY, eco_sntl_sites, eco_sntl) %>%
summarise(mean_spring_days = mean(newSWE_days), med_spring_days = median(newSWE_days),
mean_newSWE = mean(newSWE), med_newSWE = median(newSWE)) %>%
merge(.,eco_sntl_sites_med, by = "eco_sntl")
## `summarise()` has grouped output by 'site_id', 'site_name', 'state',
## 'latitude', 'longitude', 'elev', 'US_L3CODE', 'US_L3NAME', 'L3_KEY',
## 'eco_sntl_sites'. You can override using the `.groups` argument.
# # View data
# mapview(PSZ_cont_snotel_site, ycol = "latitude", xcol = "longitude", zcol = "med_spring_days",
# layer.name = "AVG Sping Days (PSZ Sites)" , crs = 4326, grid = FALSE) +
# mapview(eco_L3_4326, zcol = "L3_KEY")
# Spring SWE increases (After April 1)
spring_snow_days <- ggplot(cont_snotel_site_yr_apr1, aes(x = reorder(US_L3CODE,newSWE_days,FUN = median, na.rm = TRUE), y= newSWE_days,
fill = reorder(eco_sntl,newSWE_days,FUN = median,na.rm = TRUE))) +
geom_boxplot() +
labs(x = "EPA Level III Ecoregion", y = "Days of Increased SWE", fill = "") +
scale_y_continuous(breaks = seq(0,50,10), limits = c(0,50), expand = c(0.01, 0.01)) +
scale_x_discrete(labels = function(eco_sntl) str_wrap(eco_sntl, width = 28)) +
theme_bw()
spring_snow_days
ggplotly(spring_snow_days)
ggplot_build(spring_snow_days)$data
## [[1]]
## fill ymin lower middle upper ymax
## 1 #F8766D 0 0 0 1 2
## 2 #E58700 0 1 4 9 21
## 3 #C99800 0 0 4 9 22
## 4 #A3A500 0 1 4 9 21
## 5 #6BB100 0 1 5 10 23
## 6 #00BA38 0 3 6 10 20
## 7 #00BF7D 0 2 6 12 27
## 8 #00C0AF 0 3 7 13 28
## 9 #00BCD8 0 3 7 12 23
## 10 #00B0F6 0 2 7 12 27
## 11 #619CFF 0 3 7 13 28
## 12 #B983FF 0 3 8 13 28
## 13 #E76BF3 0 5 10 16 32
## 14 #FD61D1 0 6 11 18 36
## 15 #FF67A4 0 8 14 20 38
## outliers
## 1 3, 6, 3, 4, 5, 6, 7, 3, 6, 3, 5, 5, 6, 13, 4, 3, 3, 6, 6, 3, 3, 5, 4, 11, 4, 4, 7, 3, 6, 4, 10, 4, 6, 4, 3, 3, 7, 3, 3, 6, 3, 3, 5, 6, 5, 3, 3, 3, 6, 4, 3, 3, 3, 4
## 2 23, 24, 25, 29, 37, 32, 24, 27, 29, 26, 25, 38, 25, 25, 33, 34, 27, 22, 30, 24, 33, 29, 27, 25, 24, 38, 24, 22, 32, 26, 23
## 3 24, 23, 25, 23, 25, 24, 24, 23, 24, 32
## 4 27, 23, 24, 23, 26, 27, 22, 28, 26
## 5 28, 24, 30, 26, 37, 33, 24, 27, 25, 28
## 6 27, 23, 25, 25, 28, 27, 21, 27, 25, 33, 29, 27, 22, 23, 35, 22, 21, 21
## 7 31, 36, 30, 29, 28, 35, 29, 35, 28, 30, 28, 32, 35, 35, 29, 33
## 8 29, 36, 32, 33
## 9
## 10 29, 31, 30, 36, 29, 33, 31, 28, 30, 30, 34, 28, 29, 29, 28, 30, 31, 29, 29
## 11 33, 31, 34, 38, 33, 29, 40, 40, 30, 34, 33, 29, 30, 30, 31, 31, 29, 29, 43, 30, 32, 30, 30, 33, 30, 34, 30, 29, 29, 29, 30, 30, 34
## 12 29, 31, 29, 32, 29
## 13 34, 39, 36, 37, 33, 34, 35, 35, 37, 36, 34, 35, 34, 38, 37, 34, 34, 38, 43, 34
## 14 38
## 15 39, 39, 41, 39, 39, 40, 41, 43, 41, 41, 48, 41
## notchupper notchlower x flipped_aes PANEL group ymin_final ymax_final
## 1 0.06366001 -0.06366001 1 FALSE 1 1 0 13
## 2 4.48401062 3.51598938 2 FALSE 1 2 0 38
## 3 4.55818383 3.44181617 3 FALSE 1 3 0 32
## 4 4.58806602 3.41193398 4 FALSE 1 4 0 28
## 5 5.53593641 4.46406359 5 FALSE 1 5 0 37
## 6 6.43214996 5.56785004 6 FALSE 1 6 0 35
## 7 6.63149501 5.36850499 7 FALSE 1 7 0 36
## 8 7.53505551 6.46494449 8 FALSE 1 8 0 36
## 9 8.15720719 5.84279281 9 FALSE 1 9 0 23
## 10 7.33832123 6.66167877 10 FALSE 1 10 0 36
## 11 7.42440009 6.57559991 11 FALSE 1 11 0 43
## 12 8.59464084 7.40535916 12 FALSE 1 12 0 32
## 13 10.30218091 9.69781909 13 FALSE 1 13 0 43
## 14 11.98835832 10.01164168 14 FALSE 1 14 0 38
## 15 14.34135852 13.65864148 15 FALSE 1 15 0 48
## xmin xmax xid newx new_width weight colour size alpha shape linetype
## 1 0.625 1.375 1 1 0.75 1 grey20 0.5 NA 19 solid
## 2 1.625 2.375 2 2 0.75 1 grey20 0.5 NA 19 solid
## 3 2.625 3.375 3 3 0.75 1 grey20 0.5 NA 19 solid
## 4 3.625 4.375 4 4 0.75 1 grey20 0.5 NA 19 solid
## 5 4.625 5.375 5 5 0.75 1 grey20 0.5 NA 19 solid
## 6 5.625 6.375 6 6 0.75 1 grey20 0.5 NA 19 solid
## 7 6.625 7.375 7 7 0.75 1 grey20 0.5 NA 19 solid
## 8 7.625 8.375 8 8 0.75 1 grey20 0.5 NA 19 solid
## 9 8.625 9.375 9 9 0.75 1 grey20 0.5 NA 19 solid
## 10 9.625 10.375 10 10 0.75 1 grey20 0.5 NA 19 solid
## 11 10.625 11.375 11 11 0.75 1 grey20 0.5 NA 19 solid
## 12 11.625 12.375 12 12 0.75 1 grey20 0.5 NA 19 solid
## 13 12.625 13.375 13 13 0.75 1 grey20 0.5 NA 19 solid
## 14 13.625 14.375 14 14 0.75 1 grey20 0.5 NA 19 solid
## 15 14.625 15.375 15 15 0.75 1 grey20 0.5 NA 19 solid
# Pairwise comparison of all sites
stat.test <- cont_snotel_site_yr_apr1 %>%
mutate(US_L3CODE = as.numeric(US_L3CODE)) %>%
arrange(US_L3CODE) %>%
pairwise_t_test(newSWE_days ~ US_L3CODE, p.adjust.method = "bonferroni") %>%
add_significance(cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, 1),
symbols = c("****", "***", "**", "*", "ns"))
level1 = c(4,5,9,11,13,15,16,17,18,19,21,23,41,77,80)
level2 = c(5,9,11,13,15,16,17,18,19,21,23,41,77,80)
pairwise_plot <- ggplot(stat.test, aes(x = factor(group1, levels = level1),
y = factor(group2, levels = level2),
fill = p.adj.signif))+
geom_tile(col = "black") +
scale_fill_brewer(labels = c("1e-04", "0.001", "0.01", "0.05", "ns"),
palette = "Reds", direction = -1)+
labs(y = "EPA Level III Ecoregion", x = "EPA Level III Ecoregion", fill = "Sig. Level") +
theme_bw()
pairwise_plot
ggplotly(pairwise_plot)
#test <- ggplot_build(pairwise_plot)$data[[1]]
# boxplot and pairwise
plots <- plot_grid(
spring_snow_days + theme(legend.position="none"),
pairwise_plot,
align = 'vh',
labels = c("a.", "b."),
hjust = -0.4,
nrow = 1)
## Warning: Graphs cannot be vertically aligned unless the axis parameter is set.
## Placing graphs unaligned.
plots
legend <- get_legend(spring_snow_days + guides(fill = guide_legend(nrow = 5)) +
theme(legend.direction = "horizontal",
legend.justification="center",
legend.box.just = "bottom"))
plot_grid(plots, legend, ncol = 1, rel_heights = c(1, .3))
# Elevation vs new SWE count (March 1)
elev_newsnow <- ggplot(cont_snotel_site_apr1, aes(x = med_spring_days, y= elev,
color = reorder(eco_sntl,median_spring_SWE,na.rm = TRUE))) +
geom_point() +
#geom_smooth(method = "lm")+
#stat_smooth(method = "lm", col = "black")+
labs(x = "Median Days of Increased SWE", y = "Elevation (m)", color = "") +
scale_x_continuous(breaks = seq(0,30,10), limits = c(0,30), expand = c(0.01, 0.01)) +
scale_y_continuous(breaks = seq(500,4000,1000), limits = c(500,3750), expand = c(0.01, 0.01)) +
theme_bw()
elev_newsnow
ggplotly(elev_newsnow)
#ggplot_build(elev_newsnow)$data
plot <- plot_grid(
spring_snow_days + theme(legend.position="none"),
elev_newsnow + theme(legend.position="none"),
align = 'vh',
labels = c("a.", "b."),
hjust = -1,
nrow = 1)
plot
legend <- get_legend(spring_snow_days + guides(fill = guide_legend(nrow = 5)) +
theme(legend.direction = "horizontal",
legend.justification="center",
legend.box.just = "bottom"))
plot_grid(plot, legend, ncol = 1, rel_heights = c(1, .3))
#Determine the number of SWE events above a temperature threshold
summary_stats <- cont_snotel_spring_apr1 %>%
#filter(site_id == "1000" & (date >= "2001-04-01" & date <= "2001-04-15")) %>%
summarise(n_above_mean = sum(newSWE > 0 & temperature_mean > 0, na.rm = TRUE),
n_above_min = sum(newSWE > 0 & temperature_min > 0, na.rm = TRUE),
n_below_mean = sum(newSWE > 0 & temperature_mean <= 0, na.rm = TRUE),
n_below_min = sum(newSWE > 0 & temperature_min <=0, na.rm = TRUE),
n_newSWE = sum(newSWE > 0, na.rm = TRUE),
n = n())
above_min_pct = summary_stats$n_above_min/(summary_stats$n_newSWE)